@eikeakanam, because your post is a month old, you’ve probably already got the code for linear regression in Fortran… I’m late. 
But my most honest answer would be that for a thousand of reasons you can’t make a prediction of car prices by the use of a simple regression model. Maybe we’re just talking of some academic exercise but this can’t be useful in the real world.
If you apply a linear regression to stock market prices predicting the next number (y_pred) in a sequence, you won’t get the future price but a somewhat counterintuitive thing:
y_pred > the_most_recent_price means that the price is currently in a downtrend.
y_pred < the_most_recent_price means that the price is currently in an uptrend.
No, this is not a typing error! This mechanism is very comparable to what we observe in the moving average widely used by traders.
Second, notice that we only get information about the current trend (up or down) but not the forthcoming price (in dollars). And there’s a 50% probability that this current trend is going to change right now! In conclusion, only by the use of a simple regression model we get nothing useful and can’t purchase Twitter. 
As I’ve said in my last post in the Forum, I’m currently working on AI models in Fortran. But note that it’s September now and my last post herein is from May when I was talking about months of past work. And now in September I’m still working on this… It took me several thousand hours of work to develop an early prototype of a Fortran code predicting the prices of the crude oil, and even this model can’t work with other commodities and stock market indices without certain modifications.
The point is that the real-world model you need to develop in order to know the future prices of cars is complex as hell. But yes, I still believe the Fortran is the best existing tool for this purpose! And linear regression is an obvious starting point. 